A multiplexed single electron transistor for application in scalable solid-state quantum computing

Single Electron Transistors (SETs) are nanoscale electrometers of unprecedented sensitivity, and as such have been proposed as read-out devices in a number of quantum computer architectures. We show that the functionality of a standard SET can be multiplexed so as to operate as both read-out device and control gate for a solid-state qubit. Multiplexing in this way may be critical in lowering overall gate densities in scalable quantum computer architectures.Comment: 3 pages 3 figure

Strategies and challenges to facilitate situated learning in virtual worlds post-Second Life

Virtual worlds can establish a stimulating environment to support a situated learning approach in which students simulate a task within a safe environment. While in previous years Second Life played a major role in providing such a virtual environment, there are now more and more alternative—often OpenSim-based—solutions deployed within the educational community. By drawing parallels to social networks, we discuss two aspects: how to link individually hosted virtual worlds together in order to implement context for immersion and how to identify and avoid “fake” avatars so people behind these avatars can be held accountable for their actions

Analysis and Geometric Optimization of Single Electron Transistors for Read-Out in Solid-State Quantum Computing

The single electron transistor (SET) offers unparalled opportunities as a nano-scale electrometer, capable of measuring sub-electron charge variations. SETs have been proposed for read-out schema in solid-state quantum computing where quantum information processing outcomes depend on the location of a single electron on nearby quantum dots. In this paper we investigate various geometries of a SET in order to maximize the device's sensitivity to charge transfer between quantum dots. Through the use of finite element modeling we model the materials and geometries of an Al/Al2O3 SET measuring the state of quantum dots in the Si substrate beneath. The investigation is motivated by the quest to build a scalable quantum computer, though the methodology used is primarily that of circuit theory. As such we provide useful techniques for any electronic device operating at the classical/quantum interface.Comment: 13 pages, 17 figure

Bacteria Use Type IV Pili to Walk Upright and Detach from Surfaces

1. Department of Bioengineering, California Nano Systems Institute,University of California, Los Angeles, CA 90024, USA. 2. Department of Chemical and Biomolecular Engineering, University of Houston, Houston, TX 77204, USA. 3. Department of Materials Science and Engineering, University of Illinois, Urbana-Champaign, IL 61801, USA. 4. Department of Civil Engineering and Geological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA.Bacterial biofilms are structured multicellular communities involved in a broad range of infections. Knowing how free-swimming bacteria adapt their motility mechanisms near surfaces is crucial for understanding the transition between planktonic and biofilm phenotypes. By translating microscopy movies into searchable databases of bacterial behavior, we identified fundamental type IV pili–driven mechanisms for Pseudomonas aeruginosa surface motility involved in distinct foraging strategies. Bacteria stood upright and “walked” with trajectories optimized for two-dimensional surface exploration. Vertical orientation facilitated surface detachment and could influence biofilm morphology.Center for Nonlinear Dynamic

Gauge covariance and the fermion-photon vertex in three- and four- dimensional, massless quantum electrodynamics

In the quenched approximation, the gauge covariance properties of three vertex Ans\"{a}tze in the Schwinger-Dyson equation for the fermion self energy are analysed in three- and four- dimensional quantum electrodynamics. Based on the Cornwall-Jackiw-Tomboulis effective action, it is inferred that the spectral representation used for the vertex in the gauge technique cannot support dynamical chiral symmetry breaking. A criterion for establishing whether a given Ansatz can confer gauge covariance upon the Schwinger-Dyson equation is presented and the Curtis and Pennington Ansatz is shown to satisfy this constraint. We obtain an analytic solution of the Schwinger-Dyson equation for quenched, massless three-dimensional quantum electrodynamics for arbitrary values of the gauge parameter in the absence of dynamical chiral symmetry breaking.Comment: 17 pages, PHY-7143-TH-93, REVTE

Software Extensions to UCSF Chimera for Interactive Visualization of Large Molecular Assemblies

SummaryMany structures of large molecular assemblies such as virus capsids and ribosomes have been experimentally determined to atomic resolution. We consider four software problems that arise in interactive visualization and analysis of large assemblies: how to represent multimers efficiently, how to make cartoon representations, how to calculate contacts efficiently, and how to select subassemblies. We describe techniques and algorithms we have developed and give examples of their use. Existing molecular visualization programs work well for single protein and nucleic acid molecules and for small complexes. The methods presented here are proposed as features to add to existing programs or include in next-generation visualization software to allow easy exploration of assemblies containing tens to thousands of macromolecules. Our approach is pragmatic, emphasizing simplicity of code, reliability, and speed. The methods described have been distributed as the Multiscale extension of the UCSF Chimera (www.cgl.ucsf.edu/chimera) molecular graphics program

Fracture of Solid Wood: A Review of Structure and Properties at Different Length Scales

This paper presents a review of the fracture literature of solid wood. The review is not exhaustive and is focused on the structure and properties of wood at different length scales. Fracture of wood has been examined in all pure modes as well as mixed-Mode I and II and all directions—radial, tangential, and longitudinal. The literature has been studied at a variety of levels from molecular through cellular and growth ring to macroscopic. The major conclusions are that fracture toughness perpendicular to the grain is greater than that parallel to the grain and Mode II is greater than Mode I, within a given species. Also, fracture toughness increases with increasing density and strain rate. Defects typically reduce the strength and fracture toughness, with edge defects having a greater effect. Finally, the fracture toughness of solid wood reaches a maximum between 6 to 8% moisture content. The paper discusses how these macroscopic observations are related to the chemical composition and micro/meso-structure of wood

Dynamic Analysis of Executables to Detect and Characterize Malware

It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by executables-alleviating attempts at obfuscation as the behavior is monitored rather than the bytes of an executable. We examine several machine learning techniques for detecting malware including random forests, deep learning techniques, and liquid state machines. The experiments examine the effects of concept drift on each algorithm to understand how well the algorithms generalize to novel malware samples by testing them on data that was collected after the training data. The results suggest that each of the examined machine learning algorithms is a viable solution to detect malware-achieving between 90% and 95% class-averaged accuracy (CAA). In real-world scenarios, the performance evaluation on an operational network may not match the performance achieved in training. Namely, the CAA may be about the same, but the values for precision and recall over the malware can change significantly. We structure experiments to highlight these caveats and offer insights into expected performance in operational environments. In addition, we use the induced models to gain a better understanding about what differentiates the malware samples from the goodware, which can further be used as a forensics tool to understand what the malware (or goodware) was doing to provide directions for investigation and remediation.Comment: 9 pages, 6 Tables, 4 Figure

Neurogenesis Deep Learning

Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had their strongest impact on tasks such as image and audio processing - data processing domains in which humans have long held clear advantages over conventional algorithms. In contrast to biological neural systems, which are capable of learning continuously, deep artificial networks have a limited ability for incorporating new information in an already trained network. As a result, methods for continuous learning are potentially highly impactful in enabling the application of deep networks to dynamic data sets. Here, inspired by the process of adult neurogenesis in the hippocampus, we explore the potential for adding new neurons to deep layers of artificial neural networks in order to facilitate their acquisition of novel information while preserving previously trained data representations. Our results on the MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes lower and upper case letters and digits, demonstrate that neurogenesis is well suited for addressing the stability-plasticity dilemma that has long challenged adaptive machine learning algorithms.Comment: 8 pages, 8 figures, Accepted to 2017 International Joint Conference on Neural Networks (IJCNN 2017